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Cost/Benefit Analysis of AIoT Image Sensing for Construction Safety Monitoring

Journal of Engineering, Project, and Production Management 2024年第3期14卷 1-13页

作者： Wang, Rong-Jing Yu, Wen-Der Liao, Hsien-Chou Chang, Hsien-Kuan Lim, Zi-Yi Architecture and Building Research Institute Ministry of Interior New Taipei23143 Taiwan Department of Construction Engineering Chaoyang University of Technology Taichung413310 Taiwan Department of Computer Science and Information Technology Chaoyang University of Technology Taichung413310 Taiwan Department of Information and Communication Engineering Chaoyang University of Technology Taichung413310 Taiwan

Rapid advances in deep learning and computer vision enable traditional cloud-based decision-making through edge computing with the Artificial Intelligent Internet of Things (AIoT) image sensors (AIoT-IS), thus improving the timeliness and security of image recognition. This study is indented to investigate the potential costs and benefits of AIoT-IS applications. This study summarizes AIoT-IS application scenarios for construction safety monitoring and proposes a cost/benefit analysis method for AIoT-IS implementation projects. According to the case study results, AIoT-IS achieves significant benefits, with a Net Present Value Index (NPVI) of 19.17% and a Benefit/Cost Ratio (BCR) of 4.65 as applied to construction site safety monitoring. Interviews with domain experts also provided qualitative feedback, pointing to the directions for future research. The proposed method is applicable for the decision-making of AIoT-IS adoption and the feasibility assessment of other innovative construction technologies. Copyright © Journal of Engineering, Project, and Production Management (EPPM-Journal).

关键词： Cost benefit analysis

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An ensemble approach for classification of diabetic retinopathy in fundus image

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Multimedia Tools and Applications 2025年第12期84卷 10567-10586页

作者： Prakash, J. Vinoth kumar, B. Department of Computer Science & Engineering PSG College of Technology Tamil Nadu Coimbatore India Department of Information Technology PSG College of Technology Tamil Nadu Coimbatore India

Diabetes-oriented diabetic retinopathy impacts the blood vessels in the region of the retina to enlarge and leak blood and other fluids. In most cases, diabetic retinopathy affects both eyes. If left untreated, it would eventually lead to blindness. Diabetic Retinopathy is so widespread that around 93 million adults are affected by it on a worldwide scale. Early identification and treatment would reduce the danger of serious diabetic retinopathy visual impairment. To achieve early detection, we can make use of our advancement in image processing to diagnose how severely the patient is affected by diabetic retinopathy. Using a patient’s fundus image and modern deep learning methods, we will be able to classify the result into five categories of severity: unaffected, severe, mild, moderate, and proliferate. Based on the results, the doctor would be able to determine what type of treatment is required to further avoid vision loss. The strategy outlined in this work is to employ five well-known deep learning models, namely Inception ResNet V2, VGG19, Xception, ResNet50, DenseNet112, and integrate their outcomes successfully utilizing ensemble learning. The experimentation outcome displays that the proposed approach classifies all the classes of Diabetic Retinopathy and performs better compared to other methods with a Specificity of 92.02%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Blood vessels

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Developing a Framework for Identifying the Scenario of Drug Addiction in Bangladesh 8th

Developing a Framework for Identifying the Scenario of Drug ...

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8th International Conference on information and communication technology for Intelligent Systems, ICTIS 2024

作者： Akter, Mariam Tofael Ahmed, Md. Mazumder, Shazzad Hossain Chowdhury, Muntasir Karim Shahriar Zaman Abid, Md. Rayan, Muhammad Abu Department of Computer Science and Engineering Feni University Feni Bangladesh Department of Information and Communication Technology Comilla University Cumilla Bangladesh

ISBN: (纸本)9789819758098

Drug addiction is one of the global concerns that is getting worse. Noakhali claims that there are more drug addicts in Bangladesh than there are people. A person with a drug addiction and one without one differ greatly in terms of their social life, personal habits, family life, and state of health. Thus, with appropriate therapeutic issues, actions should be taken to prevent drug addiction. We should investigate the contributing variables to drug addiction as well as potential remedies to lower the prevalence of drug addiction. The subjects of the study are the residents of Bangladesh’s Noakhali. The majority of the drug-addicted population data is gathered from the Bangladeshi division of Chittagong’s Noakhali and Feni. They range in age from 14 to 85 years old, both male and female. We have gathered 1514 pieces of data in total. Six different algorithms—Logistic Regression, Decision Tree, Random Forest, Naive Bayes, Support Vector Machine (SVM), and KNN—have all been used, and the outcomes are contrasted. SVM had the best results out of all the algorithms: accuracy of 94.45%, precision of 99.11%, recall of 100%, F1-score of 99.9%, and ROC score of 99%. So, this paper is effective and efficient for drug addiction analysis, classify and result compare of Bangladesh. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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One Shot Learning for Edge Detection on Point Clouds

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IEEE Transactions on Visualization and computer Graphics 2025年第5期31卷 3503-3513页

作者： Tu, Zhikun Zhang, Yuhe Jia, Yiou Li, Kang Cohen-Or, Daniel Northwest University School of Information Science and Technology Xi'an China Tel Aviv University Department of Computer Science Tel Aviv Israel

Each scanner possesses its unique characteristics and exhibits its distinct sampling error distribution. Training a network on a dataset that includes data collected from different scanners is less effective than training it on data specific to a single scanner. Therefore, we present a novel one-shot learning method allowing for edge extraction on point clouds, by learning the specific data distribution of the target point cloud, and thus achieve superior results compared to networks that were trained on general data distributions. More specifically, we present how to train a lightweight network named OSFENet (One-Shot Feature Extraction Network), by designing a filtered-KNN-based surface patch representation that supports a one-shot learning framework. Additionally, we introduce an RBF_DoS module, which integrates Radial Basis Function-based Descriptor of the Surface patch, highly beneficial for the edge extraction on point clouds. The advantage of the proposed OSFENet is demonstrated through comparative analyses against 7 baselines on the ABC dataset, and its practical utility is validated by results across diverse real-scanned datasets, including indoor scenes like S3DIS dataset, and outdoor scenes such as the Semantic3D dataset and UrbanBIS dataset. © 2025 IEEE.

关键词： Radial basis function networks

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Enabling IoT Service Classification: A Machine Learning-Based Approach for Handling Classification Issues in Heterogeneous IoT Services

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IEEE Access 2023年 11卷 89024-89037页

作者： Abbasi, Mohammad Asad Chen, Yen-Lin Khan, Abdullah Ayub Memon, Zulfiqar A. Durrani, Nouman M. Yang, Jing Ku, Chin Soon Por, Lip Yee Benazir Bhutto Shaheed University Lyari Department of Computer Science and Information Technology Karachi75660 Pakistan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106344 Taiwan Sindh Madressatul Islam University Department of Computer Science Karachi74000 Pakistan -FAST Department of Computer Science Islamabad44000 Pakistan Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia Universiti Tunku Abdul Rahman Department of Computer Science Kampar31900 Malaysia

The Internet of Things (IoT) is a form of Internet-based distributed computing that allows devices and their services to interact and execute tasks for each other. Consequently, the footprint of the IoT is increasing and becoming more complex to the highest degree. This has also given birth to new IoT-enabled applications and services. Efficient service interaction and management also call for understanding and analyzing the nature of IoT services. Further, IoT services must be characterized into various classes, and different service-related attributes must be considered for the classification. This article assesses the requirements of heterogeneous IoT services by examining their interactions. Principally, heterogeneous IoT and their service interactions are targeted. The research work performs classification of IoT services into various classes. Services are classified on the basis of various attributes. The attributes reflect different characteristics of the services. This research enables improved utilization of IoT services through efficient classification of available resources using machine learning methods. To demonstrate service classification applicability, the SVM, voting classifier, and decision tree have been applied in a service-oriented environment along with different types of services. All the services in the data set were analyzed and divided into five classes. Moreover, the decision tree performed well and achieved higher accuracy values in all classes. However, the overall prediction and classification of the decision tree model were observed to be good and satisfactorily high. © 2013 IEEE.

关键词： Decision trees

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MLMO-HSM: Multi-label Multi-output Hybrid Sequential Model for multi-resident smart home activity recognition

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Journal of Ambient Intelligence and Humanized Computing 2023年第3期14卷 2313-2325页

作者： Ramanujam, E. Perumal, Thinagaran Department of Computer Science and Engineering National Institute of Technology Silchar Assam 788010 India Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang India

Smart home automation is protective and preventive measures that are taken to monitor elderly people in a non-intrusive manner using simple and pervasive sensors termed Ambient Assistive Living. The smart home produces a large volume of sensor activations to predict an elder’s health status to improve the quality of life and independent living. Machine learning techniques are very familiar and popular in recognizing single resident activities using such a sensor reading. However, multi-resident activities are more complex, and no correlation exists between sensor readings and activities. Recently deep learning and graphical models have been proposed to solve this problem, but it consumes more time to train the model. Moreover, models are primarily executed in parallel, or multi-resident activity recognition has been converted to single for classifying events. This paper proposes a Multi-label Multi-output Hybrid Sequential Model (MLMO-HSM), a feature engineering approach with a hybrid sequential model to recognize the multi-resident activities concurrently in the shortest time. Experimentation has been performed with various machine learning, graphical and deep learning models at the different sizes of the ARAS dataset to validate the efficiency of the proposed model in terms of activity recognition and computation time. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Automation

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Hybrid Authentication Using Node Trustworthy to Detect Vulnerable Nodes

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computer Systems science & Engineering 2023年第4期45卷 625-640页

作者： S.M.Udhaya Sankar S.Thanga Revathi R.Thiagarajan Department of Information Technology Velammal Insitutue of TechnologyChennai601204India Department of Networking and Communication SRM Institute of Science and TechnologyKattankulathur603203India Department of Information Technology Prathyusha Engineering CollegeThiruvallur602025India

An ad-hoc sensor network(ASN)is a group of sensing nodes that transmit data over a wireless link to a target node,direct or indirect,through a series of *** becomes a high-risk group for several security exploits due to the sensor node’s limited *** threats are more challenging to protect against than external *** nodes are grouped,and calculate each node’s trust *** trust level is the result of combining internal and external trust *** heads(CH)are chosen based on the anticipated trust *** communications are then digitally signed by the source,encoded using a key pair given by a trustworthy CH,decoded by the recipient,and supervised by *** authenticates the technique by identifying the presence of both the transmitter and the *** approach looks for a trustworthy neighboring node that meets the trust threshold condition to authenticate the key *** companion node reaffirms the node’s reliability by getting the public-key *** seeking sensor node and the certification issuer node must have a close and trusting *** results of the proposed hybrid authentication using a node trustworthy(HANT)system are modeled and tested,and the suggested approach outperforms conventional trust-based approaches in throughput,latency,lifetime,and vulnerability methods.

关键词： Ad hoc sensor network wireless security clustering cryptography key management

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An Automatic Diagnostic Tool for Autism Spectrum Disorder using Structural Magnetic Resonance Imaging and a Tailored Binary Coded Genetic Algorithm

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IEIE Transactions on Smart Processing and Computing 2024年第3期13卷 236-242页

作者： Sachnev, Vasily Suresh, Mahanand Belathur Department of Information Communication and Electronics Engineering Catholic University of Korea Korea Republic of Department of Information Science and Engineering Computing Sri Jayachamarajendra College of Engineering JSS Science and Technology University Mysuru India

In this paper, we propose an automatic diagnostic tool for autism based on machine learning and structural MRI. A set of 989 relevant features extracted from structural magnetic resonance imaging (MRI) present in the ABIDE database was used. The tool has two steps: searching for the best set of features using a tailored binary coded genetic algorithm and then using the selected set of features to train a classifier using an extreme learning machine. The trained classifier can efficiently identify autism spectrum disorder (ASD) versus healthy controls. The proposed tailored binary coded genetic algorithm uses a statistical selective crossover designed specifically for the classification problem, which significantly speeds up the genetic algorithm and improves the classification of ASD versus healthy controls. Extensive experiments with the proposed automatic autism diagnostic tool clearly indicate the advantages of the proposed method compared to previous approaches. Copyrights © 2024 The Institute of Electronics and information Engineers.

关键词： Genetic algorithms

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Performance Evaluation of Natural Language Processing Algorithms for Sentiment Analysis

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SN computer science 2024年第6期5卷 724页

作者： Silviya, S. H. Annie Faith, S. Julia Seetha, R. Hemalatha, M. Department of Computer Science and Engineering Rajalakshmi Institute of Technology (RIT) Department of Information Technology S.A.Engineering College Department of Information Technology Vellore Institute of Technology Department of Computer Science and Engineering RMD Engineering College

Sentiment analysis can be used to identify if a text’s sentiment is neutral, positive, or negative. One type of natural language processing is sentiment analysis. An interdisciplinary field encompassing linguistics, computer science, and artificial intelligence called “natural language processing” studies how computers and human language interact, with a focus on how to teach computers to process and analyze vast volumes of natural language data. Sentiment analysis, sometimes referred to as opinion mining or emotion artificial intelligence, is the methodical identification, extraction, quantification, and study of affective states and subjective data using natural language processing, text analysis, computational linguistics, and biometrics. Sentiment analysis is frequently used in online and social media platforms, voice of the customer materials (such as reviews and survey replies), and healthcare materials for marketing, customer service, and clinical medicine purposes. This paper discusses different sentiment analysis methods and selects the best approach for a certain task. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Biometrics Computational linguistics Natural language processing Sentiment analysis Text analysis

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Security Threats to xApps Access Control and E2 Interface in O-RAN

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the communications Society 2024年 5卷 1197-1203页

作者： Hung, Cheng-Feng Chen, You-Run Tseng, Chi-Heng Cheng, Shin-Ming National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei106335 Taiwan

Open Radio Access Networks (O-RANs) represent a novel wireless access network architecture that decomposes traditional RAN functions and makes them openly accessible. O-RANs enable real-time coordination, RAN performance optimization, and management through RAN Intelligent Controllers (RICs) and their related xApps. Due to the openness of O-RAN, developers have the flexibility to download various pre-developed xApps from the Internet for deployment. They can even develop their xApps to enhance the flexibility and innovation of the RAN. The current O-RAN official WG11 has defined numerous specifications for secure implementations. However, not only is accurately detecting malicious xApps a significant challenge, but the existing H-Release also does not fully adhere to the specifications in its implementation. This could potentially introduce security risks and threats. In this paper, we implement an experimental O-RAN H-Release environment and discover significant threats from the absence of specified access control permissions for xApps. Malicious attackers can illegally access APIs to utilize other services or launch attacks on legitimate xApps and E2 nodes through the E2 interface, potentially causing a complete RAN disruption. We used the identified vulnerabilities to design three attacks, providing detailed explanations and a comprehensive analysis of their impact on the system. Finally, we also submit the discovered threats to CVEs (CVE-2023-42358 and CVE-2023-41628), providing a reference for O-RAN officials to improve security in the future. © 2020 IEEE.

关键词： Real time systems

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