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Internet of Things Integrated deep-learning algorithms Monitoring and Predicting Abnormalities in Agriculture Land

INTERNET TECHNOLOGY LETTERS 2024年第0期

作者： Selvam, Prabu Krishnamoorthy, N. Kumar, S. Praveen Lokeshwaran, K. Lokesh, Madineni Syamala, Maganti Vidhya, R. G. SRM Inst Sci & Technol Sch Comp Tiruchirappalli Tamil Nadu India SRM Inst Sci & Technol Fac Sci & Humanities Dept Comp Sci & Applicat MCA Chennai Tamil Nadu India SRM Inst Sci & Technol Dept Elect & Commun Engn Kattankulathur Tamil Nadu India Madanapalle Inst Technol & Sci Dept Comp Sci & Engn Data Sci Madanapalle Andhra Pradesh India Vignans Fdn Sci Technol & Res Dept Agr Engn Guntur Andhra Pradesh India Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Guntur Andhra Pradesh India HKBK Coll Engn Dept ECE Bangalore India

The Internet of Things (IoT) plays an essential role in the majority of the recent real-time applications to speed up the process of immediate actions against abnormal behavior. Surveillance monitoring systems using CCTV and web cameras were used in earlier applications;nevertheless, they could only monitor and generate videos. The data transmission rate in the surveillance monitoring systems was poor due to the less-performance internet technologies used in the earlier systems. Since they were restricted in data transmission speed, data size carried, communication distance, and sensing range. This paper integrates the fifth-generation Internet technology (5G) IoT devices and sensors and deep-learning algorithms to improve the efficiency of agriculture surveillance monitoring systems. It helps monitor, generate, and analyze the IoT data that can be processed immediately and predict the abnormal activities happening in agriculture. Landowners can take immediate action to save and recover the crops and their domestic animals concerning abnormal actions. This paper implements a Convolution Neural Network (CNN) algorithm for analyzing the IoT data and predicting abnormal activities. The experiment result is verified and compared with the other state-of-the-art methods to evaluate the performance of the proposed CNN.

关键词： 5G IoT agricultural environment deep learning algorithm internet data transmission surveillance monitoring system

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Subjective perception analysis of active noise control algorithms in an encapsulated structure: An experimental study

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Applied Acoustics 2025年 239卷

作者： Aboutiman, Alkahf Rachman, Zulfi Oberman, Tin Alletta, Francesco Kang, Jian Karimi, Hamid Reza Ripamonti, Francesco Politecnico di Milano Department of Mechanical Engineering Milan Italy Institute for Environmental Design and Engineering The Bartlett University College London London United Kingdom

This study investigates the subjective perception of active noise control (ANC) performance, focusing on how individuals evaluate the noise reduction provided by different ANC algorithms. While the performance of the ANC algorithms has already been evaluated using objective metrics, this study aims to assess their effectiveness from a subjective perspective. In a simulated vehicle interior created using a noise box, two ANC algorithms were tested: the normalized least-mean-square (NLMS) algorithm and the hybrid selective fixed-filter active noise control normalized least-mean-square (SFANC-NLMS) algorithm. Participants were exposed to 27 stimuli, which combined three types of noise (motorcycle, street, and train), three sound pressure levels (55, 65, and 72 dB(A)), and three ANC conditions (no control, NLMS, and SFANC-NLMS). Subjective evaluations were collected using three indicators: perceived annoyance (PAY), perceived affective quality (PAQ), and perceived loudness (PLN). These metrics captured participants' impressions of the noise environment and the impact of noise control. The study is structured around three research questions (RQ1, RQ2, and RQ3), each addressing different aspects of ANC performance evaluation. In response to RQ1, the results demonstrated that the SFANC-NLMS algorithm outperformed NLMS in reducing perceived annoyance and loudness. Regarding RQ2, higher sound levels (72 dB) led to greater perceived annoyance, but sound level did not significantly alter the relationship between ANC algorithm type and perceived annoyance. Finally, in addressing RQ3, noise type influenced ANC effectiveness, with SFANC-NLMS showing more significant reductions in perceived annoyance compared to NLMS. Overall, the findings confirm that the SFANC-NLMS algorithm provides better noise reduction in encapsulated structures. © 2025 The Author(s)

关键词： Active noise control deep learning algorithm Encapsulated structure Subjective listening

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Output Only Damage Detection of a Steel Truss Bridge Based on a Semisupervised BiLSTM Modeling Scheme

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Structural Control and Health Monitoring 2025年第1期2025卷

作者： Tazwar Bakhtiyar Zahid Shohel Rana Md. Niamul Haque Department of Civil Engineering Bangladesh University of Engineering and Technology (BUET) Dhaka 1000 Bangladesh buet.ac.bd

The application of machine learning techniques in bridge health monitoring is gaining widespread popularity as it overcomes the problems faced by conventional methods. However, the scarcity of labeled data for damaged bridges in training the model acts as a hindrance. The present study proposes a data science–based novel approach for overcoming this hindrance using a semisupervised, output-only method for multiple-level damage identification of a steel truss bridge. The method employs sequence-to-sequence modeling of vehicle-induced vibration response only from a single sensor position. The authors have used a bidirectional long short-term memory (BiLSTM) network for damage feature extraction. A statistical distance metric tool, Kullback–Leibler divergence, has then been utilized for feature discrimination. The method’s efficiency is numerically investigated through a 3-D finite element model of a steel truss bridge based on real bridge specifications. A dynamic analysis using a moving vehicle is performed to obtain vehicle-induced accelerations. A total of 36 different damage scenarios have then been incorporated into the bridge. The effect of sensor position and performance because of variation in vehicle operation has also been investigated. The results show that the proposed approach successfully detects all the damage scenarios. The methodology’s performance has also been validated in detecting damages for the Old ADA Bridge benchmark data. The methodology successfully detected multiple damage states using a single sensor response.

关键词： damage detection deep learning algorithm sequence-to-sequence modeling statistical metric steel truss bridge vehicle induced response

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Diagnostic assessment of deep learning for melanocytic lesions using whole-slide pathological images

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TRANSLATIONAL ONCOLOGY 2021年第9期14卷 101161-101161页

作者： Ba, Wei Wang, Rui Yin, Guang Song, Zhigang Zou, Jinyi Zhong, Cheng Yang, Jingrun Yu, Guanzhen Yang, Hongyu Zhang, Litao Li, Chengxin Chinese Peoples Liberat Army Gen Hosp Dept Dermatol 28 Fuxing Rd Beijing 100853 Peoples R China Med Sch 28 Fuxing Rd Beijing 100853 Peoples R China Chinese Peoples Liberat Army Gen Hosp Dept Pathol Beijing Peoples R China Lenovo Res Artificial Intelligence AI Lab Beijing Peoples R China Hosp Chengdu Univ Tradit Chinese Med Chengdu Sichuan Peoples R China St Vincent Evansville Med Ctr Evansville WA USA Tianjin Chang Zheng Hosp Dept Dermatol Tianjin Peoples R China

Background: deep learning has the potential to improve diagnostic accuracy and efficiency in medical image recognition. In the current study, we developed a deep learning algorithm and assessed its performance in discriminating melanoma from nevus using whole-slide pathological images (WSIs). Methods: The deep learning algorithm was trained and validated using a set of 781 WSIs (86 melanomas, 695 nevi) from PLA General Hospital. The diagnostic performance of the algorithm was tested on an independent test set of 104 WSIs (29 melanomas, 75 nevi) from Tianjin Chang Zheng Hospital. The same test set was also diagnostically classified by 7 expert dermatopathologists. Results: The deep learning algorithm receiver operating characteristic (ROC) curve achieved a sensitivity 100% at the specificity of 94.7% in the classification of melanoma and nevus on the test set. The area under ROC curve was 0.99. Dermatopathologists achieved a mean sensitivity and specificity of 95.1% (95% confidence interval [CI]: 92.0%-98.2%) and 96.0% (95% CI: 94.2%-97.8%), respectively. At the operating point of sensitivity of 95.1%, the algorithm revealed a comparable specificity with 7 dermatopathologists (97.3% vs. 96.0%, P = 0.11). At the operating point of specificity of 96.0%, the algorithm also achieved a comparable sensitivity with 7 dermatopathologists (96.5% vs. 95.1%, P = 0.30). A more transparent and interpretable diagnosis could be generated by highlighting the regions of interest recognized by the algorithm in WSIs. Conclusion: The performance of the deep learning algorithm was on par with that of 7 expert dermatopathologists in interpreting WSIs with melanocytic lesions. By pre-screening the suspicious melanoma regions, it might serve as a supplemental diagnostic tool to improve working efficiency of pathologists.

关键词： Artificial intelligence deep learning algorithm Melanoma Nevus Whole-slide pathological images

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Microscopic identification of foodborne bacterial pathogens based on deep learning method

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FOOD CONTROL 2024年 161卷

作者： Chen, Qiong Bao, Han Li, Hui Wu, Ting Qi, Xin Zhu, Changqiang Tan, Weilong Jia, Desheng Zhou, Dongming Qi, Yong Huadong Res Inst Med & Biotech Nanjing 210002 Jiangsu Peoples R China Nanjing Med Univ Dept Orthodont Affiliated Stomatol Hosp Nanjing 210029 Jiangsu Peoples R China Nanjing Med Univ Jiangsu Prov Key Lab Oral Dis Nanjing 210029 Jiangsu Peoples R China Nanjing Med Univ Jiangsu Prov Engn Res Ctr Stomatol Translat Med Nanjing 210029 Jiangsu Peoples R China Nanjing Univ Jinling Hosp Med Sch Nanjing 210002 Jiangsu Peoples R China Second Peoples Hosp Yiyuan Cty Zibo 256100 Shandong Peoples R China

Accurate and rapid detection of foodborne bacterial pathogens is critical for food quality control. Nowadays, tracking morphological bacterial properties using microscope is still a priority at the grass-roots food supervision department due to its simplicity and low cost. However, the method requires highly professional personnel and there are certain misjudgments in the process of analysis. Automatically recognizing foodborne pathogen using deep learning algorithm to replace manual microscopy will not only reduce expert cost, artificial misjudgment, and operation time in detection, but also provide more objective and accurate identification. Here, we firstly constructed a high-quality and large-scale dataset of foodborne pathogenic bacteria, allowing the deep learningbased model to be efficiently trained and achieve accurate identification. The deep convolutional neural network-based model is capable of identifying six common foodborne pathogens, including Escherichia coli (O157:H7), Vibrio parahaemolyticus, Staphylococcus aureus, Bacillus cereus, Salmonella typhi, and Streptococcus hemolyticus, with accuracy rates of 90%-100%. This method can assist or replace the manual microscopic inspection step in traditional detection methods, and is promising to break through the traditional approach that heavily relies on manual judgment, greatly reduce the cost of experts and human errors, and provide rapid, accurate, and powerful discriminatory results in large quantities for detection.

关键词： Foodborne pathogens deep learning algorithm Convolutional neural network Identification

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Automated Segmentation of Optical Coherence Tomography Images of the Human Tympanic Membrane Using deep learning

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algorithmS 2023年第9期16卷 445-445页

作者： Oghalai, Thomas P. Long, Ryan Kim, Wihan Applegate, Brian E. Oghalai, John S. Univ Wisconsin Madison Dept Elect & Comp Engn Madison WI 53706 USA Univ Southern Calif Caruso Dept Otolaryngol Head & Neck Surg Los Angeles CA 90033 USA

Optical Coherence Tomography (OCT) is a light-based imaging modality that is used widely in the diagnosis and management of eye disease, and it is starting to become used to evaluate for ear disease. However, manual image analysis to interpret the anatomical and pathological findings in the images it provides is complicated and time-consuming. To streamline data analysis and image processing, we applied a machine learning algorithm to identify and segment the key anatomical structure of interest for medical diagnostics, the tympanic membrane. Using 3D volumes of the human tympanic membrane, we used thresholding and contour finding to locate a series of objects. We then applied TensorFlow deep learning algorithms to identify the tympanic membrane within the objects using a convolutional neural network. Finally, we reconstructed the 3D volume to selectively display the tympanic membrane. The algorithm was able to correctly identify the tympanic membrane properly with an accuracy of similar to 98% while removing most of the artifacts within the images, caused by reflections and signal saturations. Thus, the algorithm significantly improved visualization of the tympanic membrane, which was our primary objective. Machine learning approaches, such as this one, will be critical to allowing OCT medical imaging to become a convenient and viable diagnostic tool within the field of otolaryngology.

关键词： deep learning algorithm tympanic membrane Tensorflow optical coherence tomography convolutional neural network

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Arabic Tweets-Based Sentiment Analysis to Investigate the Impact of COVID-19 in KSA: A deep learning Approach

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BIG DATA AND COGNITIVE COMPUTING 2023年第1期7卷 16页

作者： Alqarni, Arwa Rahman, Atta Imam Abdulrahman Bin Faisal Univ Coll Comp Sci & Informat Technol Dept Comp Sci POB 1982 Dammam 31441 Saudi Arabia

The World Health Organization (WHO) declared the outbreak of Coronavirus disease 2019 (COVID-19) a pandemic on 11 March 2020. The evolution of this pandemic has raised global health concerns, making people worry about how to protect themselves and their families. This has greatly impacted people's sentiments. There was a dire need to investigate a large amount of social data such as tweets and others that emerged during the post-pandemic era for the assessment of people's sentiments. As a result, this study aims at Arabic tweet-based sentiment analysis considering the COVID-19 pandemic in Saudi Arabia. The datasets have been collected in two different periods in three major regions in Saudi Arabia, which are: Riyadh, Dammam, and Jeddah. Tweets were annotated with three sentiments: positive, negative, and neutral after due pre-processing. Convolutional neural networks (CNN) and bi-directional long short memory (BiLSTM) deep learning algorithms were applied for classifying the sentiment of Arabic tweets. This experiment showed that the performance of CNN achieved 92.80% accuracy. The performance of BiLSTM was scored at 91.99% in terms of accuracy. Moreover, as an outcome of this study, an overwhelming upsurge in negative sentiments were observed in the dataset during COVID-19 compared to the negative sentiments of the dataset before COVID-19. The technique has been compared with the state-of-the-art techniques in the literature and it was observed that the proposed technique is promising in terms of various performance parameters.

关键词： COVID-19 sentiment analysis Twitter deep learning algorithm CNN BiLSTM

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Development of deep learning models for microglia analyses in brain tissue using deepathology^TM STUDIO

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JOURNAL OF NEUROSCIENCE METHODS 2021年第0期364卷 109371-109371页

作者： Mohle, Luisa Bascunana, Pablo Brackhan, Mirjam Pahnke, Jens Univ Oslo UiO Dept Pathol Translat Neurodegenerat Res & Neuropathol Lab Oslo Norway Oslo Univ Hosp OUS Oslo Norway Univ Lubeck LIED Lubeck Germany Univ Latvia Fac Med Dept Pharmacol Riga Latvia

Background: Interest in artificial intelligence-driven analysis of medical images has seen a steep increase in recent years. Thus, our paper aims to promote and facilitate the use of this state-of-the-art technology to fellow researchers and clinicians. New method: We present custom deep learning models generated in deepathologyTM STUDIO without the need for background knowledge in deep learning and computer science underlined by practical suggestions. Results: We describe the general workflow in this commercially available software and present three real-world examples how to detect microglia on IBA1-stained mouse brain sections including their differences, validation results and analysis of a sample slide. Comparison with existing methods: deep-learning assisted analysis of histological images is faster than classical analysis methods, and offers a wide variety of detection possibilities that are not available using methods based on staining intensity. Conclusions: Reduced researcher bias, increased speed and extended possibilities make deep-learning assisted analysis of histological images superior to traditional analysis methods for histological images.

关键词： Artificial intelligence deep learning algorithm Histology Image analysis Machine learning Microglia Alzheimer

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Application of deep learning in Clinical Settings for Detecting and Classifying Malaria Parasites in Thin Blood Smears

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OPEN FORUM INFECTIOUS DISEASES 2023年第11期10卷 ofad469页

作者： Wang, Geng Luo, Guoju Lian, Heqing Chen, Lei Wu, Wei Liu, Hui Peking Union Med Coll Hosp Dept Clin Lab Beijing Peoples R China Beijing Xiaoying Technol Co Ltd Beijing Peoples R China Yunnan Inst Parasite Dis Cent Lab Puer Peoples R China Peking Union Med Coll Hosp Dept Clin Lab 1 Shuaifuyuan Wangfujing Beijing 100730 Peoples R China Yunnan Inst Parasite Dis Cent Lab Puer 665000 Peoples R China

Background. Scarcity of annotated image data sets of thin blood smears makes expert-level differentiation among Plasmodium species challenging. Here, we aimed to establish a deep learning algorithm for identifying and classifying malaria parasites in thin blood smears and evaluate its performance and clinical ***. You Only Look Once v7 was used as the backbone network for training the artificial intelligence algorithm model. The training, validation, and test sets for each malaria parasite category were randomly selected. A comprehensive analysis was performed on 12 708 thin blood smear images of various infective stages of 12 546 malaria parasites, including P falciparum, P vivax, P malariae, P ovale, P knowlesi, and P cynomolgi. Peripheral blood samples were obtained from 380 patients diagnosed with malaria. Additionally, blood samples from monkeys diagnosed with malaria were used to analyze P cynomolgi. The accuracy for detecting Plasmodium-infected blood cells was assessed through various evaluation ***. The total time to identify 1116 malaria parasites was 13 seconds, with an average analysis time of 0.01 seconds for each parasite in the test set. The average precision was 0.902, with a recall and precision of infected erythrocytes of 96.0% and 94.9%, respectively. Sensitivity and specificity exceeded 96.8% and 99.3%, with an area under the receiver operating characteristic curve >0.999. The highest sensitivity (97.8%) and specificity (99.8%) were observed for trophozoites and ***. The algorithm can help facilitate the clinical and morphologic examination of malaria parasites.

关键词： deep learning algorithm malaria parasite plasmodium thin blood smear YOLOv7

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Sustainable flood risk assessment using deep learning-based algorithms with a blockchain technology

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GEOCARTO INTERNATIONAL 2022年第1期38卷 2112982-2112982页

作者： Mia, Md Uzzal Rahman, Mahfuzur Elbeltagi, Ahmed Abdullah-Al-Mahbub, Md Sharma, Gitika Islam, H. M. Touhidul Pal, Subodh Chandra Costache, Romulus Islam, Abu Reza Md Towfiqul Islam, Md Monirul Chen, Ningsheng Alam, Edris Washakh, Rana Muhammad Ali Begum Rokeya Univ Dept Disaster Management Rangpur Bangladesh Int Univ Business Agr & Technol IUBAT Dept Civil Engn Dhaka Bangladesh Mansoura Univ Fac Agr Agr Engn Dept Mansoura Egypt Thapar Inst Engn & Technol Dept Comp Sci & Engn Patiala Punjab India Univ Burdwan Dept Geog Bardhaman W Bengal India Transilvania Univ Brasov Dept Civil Engn Brasov Romania Danube Delta Natl Inst Res & Dev Tulcea Romania Chinese Acad Sci Inst Mt Hazards & Environm Key Lab Mt Hazards & Earth Surface Proc Chengdu Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Acad Plateau Sci & Sustainabil Xining Peoples R China Fac Resilience Abu Dhabi U Arab Emirates Univ Chittagong Dept Geog & Environm Studies Chittagong Bangladesh Neijiang Normal Univ Sch Architecture Neijiang Sichuan Peoples R China

The couplings of convolutional neural networks (CNN) with random forest (RF), support vector machine (SVM), long short-term memory (LSTM), and extreme gradient boosting (XGBoost) ensemble algorithms were used to construct novel ensemble computational models (CNN-LSTM, CNN-XG, CNN-SVM, and CNN-RF) for flood hazard mapping in the monsoon-dominated catchment, Bangladesh. The results revealed that geology, elevation, the normalized difference vegetation index (NDVI), and rainfall are the most significant parameters in flash floods based on the Pearson correlation technique. Statistical method such as the area under the curve (AUC) was used to evaluate model performance. The CNN-RF model could be a promising tool for precisely predicting and mapping flash floods as it is outperformed the other models (AUC = 1.0). Furthermore, to meet sustainable development goals (SDGs), a blockchain-based technology is proposed to create a decentralized flood management tool for help seekers and help providers during and post floods. The suggested tool accelerates emergency rescue operations during flood events.

关键词： Blockchain relief sustainable development flash floods deep learning algorithm

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